Computer-based visualization of machine-learning models and behavior

ABSTRACT

Machine-learning models and behavior can be visualized. For example, a machine-learning model can be taught using a teaching dataset. A test input can then be provided to the machine-learning model to determine a baseline confidence-score of the machine-learning model. Next, weights for elements in the teaching dataset can be determined. An analysis dataset can be generated that includes a subset of the elements that have corresponding weights above a predefined threshold. For each overlapping element in both the analysis dataset and the test input, (i) a modified version of the test input can be generated that excludes the overlapping element, and (ii) the modified version of the test input can be provided to the machine-learning model to determine an effect of the overlapping element on the baseline confidence-score. A graphical user interface can be generated that visually depicts the test input and various elements&#39; effects on the baseline confidence-score.

REFERENCE TO RELATED APPLICATION

This claims the benefit of priority under 35 U.S.C. § 119(e) to U.S. Provisional Patent Application No. 62/590,149, filed Nov. 22, 2017, the entirety of which is hereby incorporated by reference herein.

TECHNICAL FIELD

The present disclosure relates generally to operator interface processing. More specifically, but not by way of limitation, this disclosure relates to graphical user interfaces for visualizing machine-learning models and behavior.

BACKGROUND

One example of a machine-learning model is a recurrent neural-network, which can have one or more feedback loops to allow data to propagate in both forward and backward there-through. This can allow for information to persist within the recurrent neural-network. For example, a recurrent neural-network can determine an output based at least partially on information that the recurrent neural-network has seen before, giving the recurrent neural-network the ability to use previous input to inform the output.

SUMMARY

One example of the present disclosure includes a method for visualizing machine-learning model behavior. The method can include teaching a machine-learning model using a teaching dataset that has pieces of input data correlated to output categories. Each piece of input data can be correlated to a particular output category and is formed from multiple elements. The method can include determining a plurality of weights for a plurality of elements in the teaching dataset. Each weight in the plurality of weights can correspond to a respective element in the plurality of elements and is determined based on a relationship between (i) a first value indicating a number of pieces of input data in the teaching dataset that both include the respective element and belong to a specific output category, and (ii) a second value indicating a proportion of a total number of pieces of input data in the teaching data set that have the respective element. The method can include generating an analysis dataset that includes a subset of the plurality of elements that have corresponding weights that are above a predefined threshold. The method can include providing a test input to the machine-learning model to determine a baseline confidence-score of the machine-learning model. The test input can have one or more elements in the analysis dataset. The method can include, for each overlapping element in both the analysis dataset and the test input, performing one or more operations. The operations can include generating a modified version of the test input that excludes the overlapping element. The operations can include providing the modified version of the test input to the machine-learning model to determine an effect of the overlapping element on the baseline confidence-score. The operations can include adding the overlapping element to (i) a first dataset in response to the effect of the overlapping element on the baseline confidence-score being positive, or (ii) a second dataset in response to the effect of the overlapping element on the baseline confidence-score being negative. The method can also include generating a graphical user interface. The graphical user interface can visually depicts the test input, the first dataset as having a positive effect on the baseline confidence-score, the second dataset as having a negative effect on the baseline confidence-score, or any combination of these.

Another example of the present disclosure includes a system with a processing device and a memory device that has instructions executable by the processing device for causing the processing device to perform one or more operations. The operations can include teaching a machine-learning model using a teaching dataset that has pieces of input data correlated to output categories. Each piece of input data can be correlated to a particular output category and is formed from multiple elements. The operations can include determining a plurality of weights for a plurality of elements in the teaching dataset. Each weight in the plurality of weights can correspond to a respective element in the plurality of elements and is determined based on a relationship between (i) a first value indicating a number of pieces of input data in the teaching dataset that both include the respective element and belong to a specific output category, and (ii) a second value indicating a proportion of a total number of pieces of input data in the teaching data set that have the respective element. The operations can include generating an analysis dataset that includes a subset of the plurality of elements that have corresponding weights that are above a predefined threshold. The operations can include providing a test input to the machine-learning model to determine a baseline confidence-score of the machine-learning model. The test input can have one or more elements in the analysis dataset. The operations can include, for each overlapping element in both the analysis dataset and the test input, performing one or more steps. The steps can include generating a modified version of the test input that excludes the overlapping element. The steps can include providing the modified version of the test input to the machine-learning model to determine an effect of the overlapping element on the baseline confidence-score. The steps can include adding the overlapping element to (i) a first dataset in response to the effect of the overlapping element on the baseline confidence-score being positive, or (ii) a second dataset in response to the effect of the overlapping element on the baseline confidence-score being negative. The operations can also include generating a graphical user interface. The graphical user interface can visually depicts the test input, the first dataset as having a positive effect on the baseline confidence-score, the second dataset as having a negative effect on the baseline confidence-score, or any combination of these.

Yet another example of the present disclosure includes a non-transitory computer-readable medium that has program code that is executable by a processing device for causing the processing device to perform one or more operations. The operations can include teaching a machine-learning model using a teaching dataset that has pieces of input data correlated to output categories. Each piece of input data can be correlated to a particular output category and is formed from multiple elements. The operations can include determining a plurality of weights for a plurality of elements in the teaching dataset. Each weight in the plurality of weights can correspond to a respective element in the plurality of elements and is determined based on a relationship between (i) a first value indicating a number of pieces of input data in the teaching dataset that both include the respective element and belong to a specific output category, and (ii) a second value indicating a proportion of a total number of pieces of input data in the teaching data set that have the respective element. The operations can include generating an analysis dataset that includes a subset of the plurality of elements that have corresponding weights that are above a predefined threshold. The operations can include providing a test input to the machine-learning model to determine a baseline confidence-score of the machine-learning model. The test input can have one or more elements in the analysis dataset. The operations can include, for each overlapping element in both the analysis dataset and the test input, performing one or more steps. The steps can include generating a modified version of the test input that excludes the overlapping element. The steps can include providing the modified version of the test input to the machine-learning model to determine an effect of the overlapping element on the baseline confidence-score. The steps can include adding the overlapping element to (i) a first dataset in response to the effect of the overlapping element on the baseline confidence-score being positive, or (ii) a second dataset in response to the effect of the overlapping element on the baseline confidence-score being negative. The operations can also include generating a graphical user interface. The graphical user interface can visually depicts the test input, the first dataset as having a positive effect on the baseline confidence-score, the second dataset as having a negative effect on the baseline confidence-score, or any combination of these.

This summary is not intended to identify key or essential features of the claimed subject matter, nor is it intended to be used in isolation to determine the scope of the claimed subject matter. The subject matter should be understood by reference to appropriate portions of the entire specification, any or all drawings, and each claim.

The foregoing, together with other features and examples, will become more apparent upon referring to the following specification, claims, and accompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

The present disclosure is described in conjunction with the appended figures:

FIG. 1 is a block diagram of an example of the hardware components of a computing system according to some aspects.

FIG. 2 is an example of devices that can communicate with each other over an exchange system and via a network according to some aspects.

FIG. 3 is a block diagram of a model of an example of a communications protocol system according to some aspects.

FIG. 4 is a hierarchical diagram of an example of a communications grid computing system including a variety of control and worker nodes according to some aspects.

FIG. 5 is a flow chart of an example of a process for adjusting a communications grid or a work project in a communications grid after a failure of a node according to some aspects.

FIG. 6 is a block diagram of a portion of a communications grid computing system including a control node and a worker node according to some aspects.

FIG. 7 is a flow chart of an example of a process for executing a data analysis or processing project according to some aspects.

FIG. 8 is a block diagram including components of an Event Stream Processing Engine (ESPE) according to some aspects.

FIG. 9 is a flow chart of an example of a process including operations performed by an event stream processing engine according to some aspects.

FIG. 10 is a block diagram of an ESP system interfacing between a publishing device and multiple event subscribing devices according to some aspects.

FIG. 11 is a flow chart of an example of a process for computer-based visualization of machine-learning models and behavior according to some aspects.

FIG. 12 is a table of an example of elements with corresponding weights according to some aspects.

FIG. 13 is a table of an example of elements sorted by weight according to some aspects.

FIG. 14 is an example of pseudo code for implementing some aspects of the present disclosure.

FIG. 15 is an example of pseudo code for implementing some aspects of the present disclosure.

FIG. 16 is a block diagram of an example of a process for removing elements from a test input to influence the classification outputs of a machine-learning model according to some examples.

FIG. 17 is a table of an example of processor execution-times associated with an analysis-dataset approach and a brute-force approach according to some aspects.

FIG. 18 is an example of one or more graphical user interfaces for visualizing machine-learning models and behavior according to some aspects.

FIG. 19 is an example of another graphical user interface for visualizing machine-learning models and behavior according to some aspects.

In the appended figures, similar components or features can have the same reference label. Further, various components of the same type can be distinguished by following the reference label by a dash and a second label that distinguishes among the similar components. If only the first reference label is used in the specification, the description is applicable to any one of the similar components having the same first reference label irrespective of the second reference label.

DETAILED DESCRIPTION

Currently, most machine-learning models are poorly understood and are considered “black boxes.” For example, while the general process of teaching and using machine-learning models is known, a deeper understanding of the inner workings of particular machine-learning models is often lacking. And as machine-learning models continue to grow in size and complexity, it is becoming increasingly difficult to understand why machine-learning models are producing certain results. This presents a variety of challenges for designers of machine-learning models. For example, if a designer does not understand how a machine-learning model fundamentally operates, the designer may be unable to optimize the machine-learning model for the technical environment on which the machine-learning model will execute. This often leads to machine-learning models that are overly complex or otherwise unsuitable for the technical environment on which they execute, resulting in processing errors, unnecessary memory-consumption, memory errors, increased latency, and other technical problems. As another example, if a designer does not understand how a machine-learning model fundamentally operates, the designer may be unable to optimize the machine-learning model for performing a particular task. This often results in unnecessarily complex machine-learning models that consume large amounts of processing cycles and memory to achieve results that could be more efficiently obtained if the machine-learning models were optimized for the task at hand.

Some examples of the present disclosure overcome one or more of the abovementioned issues by providing an intuitive, easy-to-use graphical user interface (GUI) that can enable computer systems to provide a better understanding of how a machine-learning model is operating, why the machine-learning model is making certain decisions, and how the machine-learning model produces its outputs. This may lead to a better understanding of how to teach and build machine-learning models that are more efficient, robust, accurate, and tailored to the underlying technical environment in which they execute. For example, the GUI can provide information that enables a designer to determine (i) why a machine-learning model returns a certain output; (ii) how input values influence the output of the machine-learning model; and (iii) how changing the values of the input affect the output of the machine-learning model. In one particular example, a designer of a machine-learning model can use the GUI to review which inputs (e.g., words, portions of an image, etc.) influence the accuracy of the machine-learning model and which inputs do not. If certain inputs are insufficiently influencing the output, or are overly influencing the output, the designer can adjust node weights, connections, or other aspects of the machine-learning model until a desirable result is achieved. But without the GUI, the designer may be unable to even identify the problem.

In some examples, the GUI can provide information that enables a designer of a machine-learning model to optimize the machine-learning model to reduce (i) the number of processing cycles executed by the machine-learning model (e.g., if a machine-learning model overfits the training dataset, the number of processing cycles can be decreased for the machine-learning model to generalize unseen data); (ii) the amount of memory consumed by the machine-learning model (e.g., for some tasks, a smaller machine-learning model may be sufficiently accurate and consume significantly less RAM and disk space than a larger machine-learning model); (iii) the amount of memory accesses performed by the machine-learning model; or (iv) any combination of these. As a particular example, a designer of a machine-learning model can use the GUI to determine that certain inputs minimally influence the output of the machine-learning model. So, the designer can adjust (e.g., remove) nodes or hidden layers related to these inputs from the machine-learning model to reduce the amount of unnecessary processing that is performed by the machine-learning model.

These illustrative examples are given to introduce the reader to the general subject matter discussed here and are not intended to limit the scope of the disclosed concepts. The following sections describe various additional features and examples with reference to the drawings in which like numerals indicate like elements but, like the illustrative examples, should not be used to limit the present disclosure.

FIGS. 1-10 depict examples of systems and methods usable for computer-based visualization of machine-learning models and behavior according to some aspects. For example, FIG. 1 is a block diagram of an example of the hardware components of a computing system according to some aspects. Data transmission network 100 is a specialized computer system that may be used for processing large amounts of data where a large number of computer processing cycles are required.

Data transmission network 100 may also include computing environment 114. Computing environment 114 may be a specialized computer or other machine that processes the data received within the data transmission network 100. The computing environment 114 may include one or more other systems. For example, computing environment 114 may include a database system 118 or a communications grid 120. The computing environment 114 can include one or more processing devices (e.g., distributed over one or more networks or otherwise in communication with one another) that, in some examples, can collectively be referred to as a processor or a processing device.

Some machine-learning approaches may be more efficiently and speedily executed and processed with machine-learning specific processors (e.g., not a generic CPU). Such processors may also provide an energy savings when compared to generic CPUs. For example, some of these processors can include a graphical processing unit (GPU), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), an artificial intelligence (AI) accelerator, a neural computing core, a neural computing engine, a neural processing unit, a purpose-built chip architecture for deep learning, and/or some other machine-learning specific processor that implements a machine learning approach or one or more neural networks using semiconductor (e.g., silicon (Si), gallium arsenide (GaAs)) devices. Furthermore, these processors may also be employed in heterogeneous computing architectures with a number of and a variety of different types of cores, engines, nodes, and/or layers to achieve various energy efficiencies, processing speed improvements, data communication speed improvements, and/or data efficiency targets and improvements throughout various parts of the system when compared to a homogeneous computing architecture that employs CPUs for general purpose computing.

Data transmission network 100 can include one or more network devices 102. Network devices 102 may include client devices that can communicate with computing environment 114. For example, network devices 102 may send data to the computing environment 114 to be processed, may send communications to the computing environment 114 to control different aspects of the computing environment or the data it is processing, among other reasons. Network devices 102 may interact with the computing environment 114 through a number of ways, such as, for example, over one or more networks 108.

In some examples, network devices 102 may provide a large amount of data, either all at once or streaming over a period of time (e.g., using event stream processing (ESP)), to the computing environment 114 via networks 108. For example, the network devices 102 can transmit electronic messages for use in computer-based visualization of machine-learning models and behavior, all at once or streaming over a period of time, to the computing environment 114 via networks 108.

The network devices 102 may include network computers, sensors, databases, or other devices that may transmit or otherwise provide data to computing environment 114. For example, network devices 102 may include local area network devices, such as routers, hubs, switches, or other computer networking devices. These devices may provide a variety of stored or generated data, such as network data or data specific to the network devices 102 themselves. Network devices 102 may also include sensors that monitor their environment or other devices to collect data regarding that environment or those devices, and such network devices 102 may provide data they collect over time. Network devices 102 may also include devices within the internet of things, such as devices within a home automation network. Some of these devices may be referred to as edge devices, and may involve edge-computing circuitry. Data may be transmitted by network devices 102 directly to computing environment 114 or to network-attached data stores, such as network-attached data stores 110 for storage so that the data may be retrieved later by the computing environment 114 or other portions of data transmission network 100. For example, the network devices 102 can transmit data usable for computer-based visualization of machine-learning models and behavior to a network-attached data store 110 for storage. The computing environment 114 may later retrieve the data from the network-attached data store 110 and use the data to visualize machine-learning model behavior.

Network-attached data stores 110 can store data to be processed by the computing environment 114 as well as any intermediate or final data generated by the computing system in non-volatile memory. But in certain examples, the configuration of the computing environment 114 allows its operations to be performed such that intermediate and final data results can be stored solely in volatile memory (e.g., RAM), without a requirement that intermediate or final data results be stored to non-volatile types of memory (e.g., disk). This can be useful in certain situations, such as when the computing environment 114 receives ad hoc queries from a user and when responses, which are generated by processing large amounts of data, need to be generated dynamically (e.g., on the fly). In this situation, the computing environment 114 may be configured to retain the processed information within memory so that responses can be generated for the user at different levels of detail as well as allow a user to interactively query against this information.

Network-attached data stores 110 may store a variety of different types of data organized in a variety of different ways and from a variety of different sources. For example, network-attached data stores may include storage other than primary storage located within computing environment 114 that is directly accessible by processors located therein. Network-attached data stores may include secondary, tertiary or auxiliary storage, such as large hard drives, servers, virtual memory, among other types. Storage devices may include portable or non-portable storage devices, optical storage devices, and various other mediums capable of storing, containing data. A machine-readable storage medium or computer-readable storage medium may include a non-transitory medium in which data can be stored and that does not include carrier waves or transitory electronic communications. Examples of a non-transitory medium may include, for example, a magnetic disk or tape, optical storage media such as compact disk or digital versatile disk, flash memory, memory or memory devices. A computer-program product may include code or machine-executable instructions that may represent a procedure, a function, a subprogram, a program, a routine, a subroutine, a module, a software package, a class, or any combination of instructions, data structures, or program statements. A code segment may be coupled to another code segment or a hardware circuit by passing or receiving information, data, arguments, parameters, or memory contents. Information, arguments, parameters, data, etc. may be passed, forwarded, or transmitted via any suitable means including memory sharing, message passing, token passing, network transmission, among others. Furthermore, the data stores may hold a variety of different types of data. For example, network-attached data stores 110 may hold unstructured (e.g., raw) data.

The unstructured data may be presented to the computing environment 114 in different forms such as a flat file or a conglomerate of data records, and may have data values and accompanying time stamps. The computing environment 114 may be used to analyze the unstructured data in a variety of ways to determine the best way to structure (e.g., hierarchically) that data, such that the structured data is tailored to a type of further analysis that a user wishes to perform on the data. For example, after being processed, the unstructured time-stamped data may be aggregated by time (e.g., into daily time period units) to generate time series data or structured hierarchically according to one or more dimensions (e.g., parameters, attributes, or variables). For example, data may be stored in a hierarchical data structure, such as a relational online analytical processing (ROLAP) or multidimensional online analytical processing (MOLAP) database, or may be stored in another tabular form, such as in a flat-hierarchy form.

Data transmission network 100 may also include one or more server farms 106. Computing environment 114 may route select communications or data to the sever farms 106 or one or more servers within the server farms 106. Server farms 106 can be configured to provide information in a predetermined manner. For example, server farms 106 may access data to transmit in response to a communication. Server farms 106 may be separately housed from each other device within data transmission network 100, such as computing environment 114, or may be part of a device or system.

Server farms 106 may host a variety of different types of data processing as part of data transmission network 100. Server farms 106 may receive a variety of different data from network devices, from computing environment 114, from cloud network 116, or from other sources. The data may have been obtained or collected from one or more websites, sensors, as inputs from a control database, or may have been received as inputs from an external system or device. Server farms 106 may assist in processing the data by turning raw data into processed data based on one or more rules implemented by the server farms. For example, sensor data may be analyzed to determine changes in an environment over time or in real-time.

Data transmission network 100 may also include one or more cloud networks 116. Cloud network 116 may include a cloud infrastructure system that provides cloud services. In certain examples, services provided by the cloud network 116 may include a host of services that are made available to users of the cloud infrastructure system on demand. Cloud network 116 is shown in FIG. 1 as being connected to computing environment 114 (and therefore having computing environment 114 as its client or user), but cloud network 116 may be connected to or utilized by any of the devices in FIG. 1. Services provided by the cloud network 116 can dynamically scale to meet the needs of its users. The cloud network 116 may include one or more computers, servers, or systems. In some examples, the computers, servers, or systems that make up the cloud network 116 are different from the user's own on-premises computers, servers, or systems. For example, the cloud network 116 may host an application, and a user may, via a communication network such as the Internet, order and use the application on demand. In some examples, the cloud network 116 may host an application for computer-based visualization of machine-learning models and behavior.

While each device, server, and system in FIG. 1 is shown as a single device, multiple devices may instead be used. For example, a set of network devices can be used to transmit various communications from a single user, or remote server 140 may include a server stack. As another example, data may be processed as part of computing environment 114.

Each communication within data transmission network 100 (e.g., between client devices, between a device and connection management system 150, between server farms 106 and computing environment 114, or between a server and a device) may occur over one or more networks 108. Networks 108 may include one or more of a variety of different types of networks, including a wireless network, a wired network, or a combination of a wired and wireless network. Examples of suitable networks include the Internet, a personal area network, a local area network (LAN), a wide area network (WAN), or a wireless local area network (WLAN). A wireless network may include a wireless interface or combination of wireless interfaces. As an example, a network in the one or more networks 108 may include a short-range communication channel, such as a Bluetooth or a Bluetooth Low Energy channel. A wired network may include a wired interface. The wired or wireless networks may be implemented using routers, access points, bridges, gateways, or the like, to connect devices in the network 108. The networks 108 can be incorporated entirely within or can include an intranet, an extranet, or a combination thereof. In one example, communications between two or more systems or devices can be achieved by a secure communications protocol, such as secure sockets layer (SSL) or transport layer security (TLS). In addition, data or transactional details may be encrypted.

Some aspects may utilize the Internet of Things (IoT), where things (e.g., machines, devices, phones, sensors) can be connected to networks and the data from these things can be collected and processed within the things or external to the things. For example, the IoT can include sensors in many different devices, and high value analytics can be applied to identify hidden relationships and drive increased efficiencies. This can apply to both big data analytics and real-time (e.g., ESP) analytics.

As noted, computing environment 114 may include a communications grid 120 and a transmission network database system 118. Communications grid 120 may be a grid-based computing system for processing large amounts of data. The transmission network database system 118 may be for managing, storing, and retrieving large amounts of data that are distributed to and stored in the one or more network-attached data stores 110 or other data stores that reside at different locations within the transmission network database system 118. The computing nodes in the communications grid 120 and the transmission network database system 118 may share the same processor hardware, such as processors that are located within computing environment 114.

In some examples, the computing environment 114, a network device 102, or both can implement one or more processes for computer-based visualization of machine-learning models and behavior. For example, the computing environment 114, a network device 102, or both can implement one or more versions of the processes discussed with respect to any of the figures.

FIG. 2 is an example of devices that can communicate with each other over an exchange system and via a network according to some aspects. As noted, each communication within data transmission network 100 may occur over one or more networks. System 200 includes a network device 204 configured to communicate with a variety of types of client devices, for example client devices 230, over a variety of types of communication channels.

As shown in FIG. 2, network device 204 can transmit a communication over a network (e.g., a cellular network via a base station 210). In some examples, the communication can include times series data. The communication can be routed to another network device, such as network devices 205-209, via base station 210. The communication can also be routed to computing environment 214 via base station 210. In some examples, the network device 204 may collect data either from its surrounding environment or from other network devices (such as network devices 205-209) and transmit that data to computing environment 214.

Although network devices 204-209 are shown in FIG. 2 as a mobile phone, laptop computer, tablet computer, temperature sensor, motion sensor, and audio sensor respectively, the network devices may be or include sensors that are sensitive to detecting aspects of their environment. For example, the network devices may include sensors such as water sensors, power sensors, electrical current sensors, chemical sensors, optical sensors, pressure sensors, geographic or position sensors (e.g., GPS), velocity sensors, acceleration sensors, flow rate sensors, among others. Examples of characteristics that may be sensed include force, torque, load, steach, position, temperature, air pressure, fluid flow, chemical properties, resistance, electromagnetic fields, radiation, irradiance, proximity, acoustics, moisture, distance, speed, vibrations, acceleration, electrical potential, and electrical current, among others. The sensors may be mounted to various components used as part of a variety of different types of systems. The network devices may detect and record data related to the environment that it monitors, and transmit that data to computing environment 214.

The network devices 204-209 may also perform processing on data it collects before transmitting the data to the computing environment 214, or before deciding whether to transmit data to the computing environment 214. For example, network devices 204-209 may determine whether data collected meets certain rules, for example by comparing data or values calculated from the data and comparing that data to one or more thresholds. The network devices 204-209 may use this data or comparisons to determine if the data is to be transmitted to the computing environment 214 for further use or processing. In some examples, the network devices 204-209 can pre-process the data prior to transmitting the data to the computing environment 214. For example, the network devices 204-209 can reformat the data before transmitting the data to the computing environment 214 for further processing (e.g., analyzing the data to generate a GUI for visualizing machine-learning models and behavior).

Computing environment 214 may include machines 220, 240. Although computing environment 214 is shown in FIG. 2 as having two machines 220, 240, computing environment 214 may have only one machine or may have more than two machines. The machines 220, 240 that make up computing environment 214 may include specialized computers, servers, or other machines that are configured to individually or collectively process large amounts of data. The computing environment 214 may also include storage devices that include one or more databases of structured data, such as data organized in one or more hierarchies, or unstructured data. The databases may communicate with the processing devices within computing environment 214 to distribute data to them. Since network devices may transmit data to computing environment 214, that data may be received by the computing environment 214 and subsequently stored within those storage devices. Data used by computing environment 214 may also be stored in data stores 235, which may also be a part of or connected to computing environment 214.

Computing environment 214 can communicate with various devices via one or more routers 225 or other inter-network or intra-network connection components. For example, computing environment 214 may communicate with client devices 230 via one or more routers 225. Computing environment 214 may collect, analyze or store data from or pertaining to communications, client device operations, client rules, or user-associated actions stored at one or more data stores 235. Such data may influence communication routing to the devices within computing environment 214, how data is stored or processed within computing environment 214, among other actions.

Notably, various other devices can further be used to influence communication routing or processing between devices within computing environment 214 and with devices outside of computing environment 214. For example, as shown in FIG. 2, computing environment 214 may include a machine 240 that is a web server. Computing environment 214 can retrieve data of interest, such as client information (e.g., product information, client rules, etc.), technical product details, news, blog posts, e-mails, forum posts, electronic documents, social media posts (e.g., Twitter™ posts or Facebook™ posts), time series data, and so on.

In addition to computing environment 214 collecting data (e.g., as received from network devices, such as sensors, and client devices or other sources) to be processed as part of a big data analytics project, it may also receive data in real time as part of a streaming analytics environment. As noted, data may be collected using a variety of sources as communicated via different kinds of networks or locally. Such data may be received on a real-time streaming basis. For example, network devices 204-209 may receive data periodically and in real time from a web server or other source. Devices within computing environment 214 may also perform pre-analysis on data it receives to determine if the data received should be processed as part of an ongoing project. For example, as part of a project for computer-based visualization of machine-learning models and behavior from data, the computing environment 214 can perform a pre-analysis of the data. The pre-analysis can include determining whether the data is in a correct format and, if not, reformatting the data into the correct format.

FIG. 3 is a block diagram of a model of an example of a communications protocol system according to some aspects. More specifically, FIG. 3 identifies operation of a computing environment in an Open Systems Interaction model that corresponds to various connection components. The model 300 shows, for example, how a computing environment, such as computing environment (or computing environment 214 in FIG. 2) may communicate with other devices in its network, and control how communications between the computing environment and other devices are executed and under what conditions.

The model 300 can include layers 302-314. The layers 302-314 are arranged in a stack. Each layer in the stack serves the layer one level higher than it (except for the application layer, which is the highest layer), and is served by the layer one level below it (except for the physical layer 302, which is the lowest layer). The physical layer 302 is the lowest layer because it receives and transmits raw bites of data, and is the farthest layer from the user in a communications system. On the other hand, the application layer is the highest layer because it interacts directly with a software application.

As noted, the model 300 includes a physical layer 302. Physical layer 302 represents physical communication, and can define parameters of that physical communication. For example, such physical communication may come in the form of electrical, optical, or electromagnetic communications. Physical layer 302 also defines protocols that may control communications within a data transmission network.

Link layer 304 defines links and mechanisms used to transmit (e.g., move) data across a network. The link layer manages node-to-node communications, such as within a grid-computing environment. Link layer 304 can detect and correct errors (e.g., transmission errors in the physical layer 302). Link layer 304 can also include a media access control (MAC) layer and logical link control (LLC) layer.

Network layer 306 can define the protocol for routing within a network. In other words, the network layer coordinates transferring data across nodes in a same network (e.g., such as a grid-computing environment). Network layer 306 can also define the processes used to structure local addressing within the network.

Transport layer 308 can manage the transmission of data and the quality of the transmission or receipt of that data. Transport layer 308 can provide a protocol for transferring data, such as, for example, a Transmission Control Protocol (TCP). Transport layer 308 can assemble and disassemble data frames for transmission. The transport layer can also detect transmission errors occurring in the layers below it.

Session layer 310 can establish, maintain, and manage communication connections between devices on a network. In other words, the session layer controls the dialogues or nature of communications between network devices on the network. The session layer may also establish checkpointing, adjournment, termination, and restart procedures.

Presentation layer 312 can provide translation for communications between the application and network layers. In other words, this layer may encrypt, decrypt or format data based on data types known to be accepted by an application or network layer.

Application layer 314 interacts directly with software applications and end users, and manages communications between them. Application layer 314 can identify destinations, local resource states or availability or communication content or formatting using the applications.

For example, a communication link can be established between two devices on a network. One device can transmit an analog or digital representation of an electronic message that includes a data set to the other device. The other device can receive the analog or digital representation at the physical layer 302. The other device can transmit the data associated with the electronic message through the remaining layers 304-314. The application layer 314 can receive data associated with the electronic message. The application layer 314 can identify one or more applications, such as an application for computer-based visualization of machine-learning models and behavior, to which to transmit data associated with the electronic message. The application layer 314 can transmit the data to the identified application.

Intra-network connection components 322, 324 can operate in lower levels, such as physical layer 302 and link layer 304, respectively. For example, a hub can operate in the physical layer, a switch can operate in the physical layer, and a router can operate in the network layer. Inter-network connection components 326, 328 are shown to operate on higher levels, such as layers 306-314. For example, routers can operate in the network layer and network devices can operate in the transport, session, presentation, and application layers.

A computing environment 330 can interact with or operate on, in various examples, one, more, all or any of the various layers. For example, computing environment 330 can interact with a hub (e.g., via the link layer) to adjust which devices the hub communicates with. The physical layer 302 may be served by the link layer 304, so it may implement such data from the link layer 304. For example, the computing environment 330 may control which devices from which it can receive data. For example, if the computing environment 330 knows that a certain network device has turned off, broken, or otherwise become unavailable or unreliable, the computing environment 330 may instruct the hub to prevent any data from being transmitted to the computing environment 330 from that network device. Such a process may be beneficial to avoid receiving data that is inaccurate or that has been influenced by an uncontrolled environment. As another example, computing environment 330 can communicate with a bridge, switch, router or gateway and influence which device within the system (e.g., system 200) the component selects as a destination. In some examples, computing environment 330 can interact with various layers by exchanging communications with equipment operating on a particular layer by routing or modifying existing communications. In another example, such as in a grid-computing environment, a node may determine how data within the environment should be routed (e.g., which node should receive certain data) based on certain parameters or information provided by other layers within the model.

The computing environment 330 may be a part of a communications grid environment, the communications of which may be implemented as shown in the protocol of FIG. 3. For example, referring back to FIG. 2, one or more of machines 220 and 240 may be part of a communications grid-computing environment. A gridded computing environment may be employed in a distributed system with non-interactive workloads where data resides in memory on the machines, or compute nodes. In such an environment, analytic code, instead of a database management system, can control the processing performed by the nodes. Data is co-located by pre-distributing it to the grid nodes, and the analytic code on each node loads the local data into memory. Each node may be assigned a particular task, such as a portion of a processing project, or to organize or control other nodes within the grid. For example, each node may be assigned a portion of a processing task for computer-based visualization of machine-learning models and behavior.

FIG. 4 is a hierarchical diagram of an example of a communications grid computing system 400 including a variety of control and worker nodes according to some aspects. Communications grid computing system 400 includes three control nodes and one or more worker nodes. Communications grid computing system 400 includes control nodes 402, 404, and 406. The control nodes are communicatively connected via communication paths 451, 453, and 455. The control nodes 402-406 may transmit information (e.g., related to the communications grid or notifications) to and receive information from each other. Although communications grid computing system 400 is shown in FIG. 4 as including three control nodes, the communications grid may include more or less than three control nodes.

Communications grid computing system 400 (which can be referred to as a “communications grid”) also includes one or more worker nodes. Shown in FIG. 4 are six worker nodes 410-420. Although FIG. 4 shows six worker nodes, a communications grid can include more or less than six worker nodes. The number of worker nodes included in a communications grid may be dependent upon how large the project or data set is being processed by the communications grid, the capacity of each worker node, the time designated for the communications grid to complete the project, among others. Each worker node within the communications grid computing system 400 may be connected (wired or wirelessly, and directly or indirectly) to control nodes 402-406. Each worker node may receive information from the control nodes (e.g., an instruction to perform work on a project) and may transmit information to the control nodes (e.g., a result from work performed on a project). Furthermore, worker nodes may communicate with each other directly or indirectly. For example, worker nodes may transmit data between each other related to a job being performed or an individual task within a job being performed by that worker node. In some examples, worker nodes may not be connected (communicatively or otherwise) to certain other worker nodes. For example, a worker node 410 may only be able to communicate with a particular control node 402. The worker node 410 may be unable to communicate with other worker nodes 412-420 in the communications grid, even if the other worker nodes 412-420 are controlled by the same control node 402.

A control node 402-406 may connect with an external device with which the control node 402-406 may communicate (e.g., a communications grid user, such as a server or computer, may connect to a controller of the grid). For example, a server or computer may connect to control nodes 402-406 and may transmit a project or job to the node, such as a project or job related to visualization of machine-learning models and behavior. The project may include the data set. The data set may be of any size and can include teaching data. Once the control node 402-406 receives such a project including a large data set, the control node may distribute the data set or projects related to the data set to be performed by worker nodes. Alternatively, for a project including a large data set, the data set may be receive or stored by a machine other than a control node 402-406 (e.g., a Hadoop data node).

Control nodes 402-406 can maintain knowledge of the status of the nodes in the grid (e.g., grid status information), accept work requests from clients, subdivide the work across worker nodes, and coordinate the worker nodes, among other responsibilities. Worker nodes 412-420 may accept work requests from a control node 402-406 and provide the control node with results of the work performed by the worker node. A grid may be started from a single node (e.g., a machine, computer, server, etc.). This first node may be assigned or may start as the primary control node 402 that will control any additional nodes that enter the grid.

When a project is submitted for execution (e.g., by a client or a controller of the grid) it may be assigned to a set of nodes. After the nodes are assigned to a project, a data structure (e.g., a communicator) may be created. The communicator may be used by the project for information to be shared between the project code running on each node. A communication handle may be created on each node. A handle, for example, is a reference to the communicator that is valid within a single process on a single node, and the handle may be used when requesting communications between nodes.

A control node, such as control node 402, may be designated as the primary control node. A server, computer or other external device may connect to the primary control node. Once the control node 402 receives a project, the primary control node may distribute portions of the project to its worker nodes for execution. For example, a project for visualizing machine-learning models and behavior can be initiated on communications grid computing system 400. A primary control node can control the work to be performed for the project in order to complete the project as requested or instructed. The primary control node may distribute work to the worker nodes 412-420 based on various factors, such as which subsets or portions of projects may be completed most efficiently and in the correct amount of time. For example, a worker node 412 may automatically generate a GUI for visualizing machine-learning models and behavior using at least a portion of data that is already local (e.g., stored on) the worker node. The primary control node also coordinates and processes the results of the work performed by each worker node 412-420 after each worker node 412-420 executes and completes its job. For example, the primary control node may receive a result from one or more worker nodes 412-420, and the primary control node may organize (e.g., collect and assemble) the results received and compile them to produce a complete result for the project received from the end user.

Any remaining control nodes, such as control nodes 404, 406, may be assigned as backup control nodes for the project. In an example, backup control nodes may not control any portion of the project. Instead, backup control nodes may serve as a backup for the primary control node and take over as primary control node if the primary control node were to fail. If a communications grid were to include only a single control node 402, and the control node 402 were to fail (e.g., the control node is shut off or breaks) then the communications grid as a whole may fail and any project or job being run on the communications grid may fail and may not complete. While the project may be run again, such a failure may cause a delay (severe delay in some cases, such as overnight delay) in completion of the project. Therefore, a grid with multiple control nodes 402-406, including a backup control node, may be beneficial.

In some examples, the primary control node may open a pair of listening sockets to add another node or machine to the grid. A socket may be used to accept work requests from clients, and the second socket may be used to accept connections from other grid nodes. The primary control node may be provided with a list of other nodes (e.g., other machines, computers, servers, etc.) that can participate in the grid, and the role that each node can fill in the grid. Upon startup of the primary control node (e.g., the first node on the grid), the primary control node may use a network protocol to start the server process on every other node in the grid. Command line parameters, for example, may inform each node of one or more pieces of information, such as: the role that the node will have in the grid, the host name of the primary control node, the port number on which the primary control node is accepting connections from peer nodes, among others. The information may also be provided in a configuration file, transmitted over a secure shell tunnel, recovered from a configuration server, among others. While the other machines in the grid may not initially know about the configuration of the grid, that information may also be sent to each other node by the primary control node. Updates of the grid information may also be subsequently sent to those nodes.

For any control node other than the primary control node added to the grid, the control node may open three sockets. The first socket may accept work requests from clients, the second socket may accept connections from other grid members, and the third socket may connect (e.g., permanently) to the primary control node. When a control node (e.g., primary control node) receives a connection from another control node, it first checks to see if the peer node is in the list of configured nodes in the grid. If it is not on the list, the control node may clear the connection. If it is on the list, it may then attempt to authenticate the connection. If authentication is successful, the authenticating node may transmit information to its peer, such as the port number on which a node is listening for connections, the host name of the node, information about how to authenticate the node, among other information. When a node, such as the new control node, receives information about another active node, it can check to see if it already has a connection to that other node. If it does not have a connection to that node, it may then establish a connection to that control node.

Any worker node added to the grid may establish a connection to the primary control node and any other control nodes on the grid. After establishing the connection, it may authenticate itself to the grid (e.g., any control nodes, including both primary and backup, or a server or user controlling the grid). After successful authentication, the worker node may accept configuration information from the control node.

When a node joins a communications grid (e.g., when the node is powered on or connected to an existing node on the grid or both), the node is assigned (e.g., by an operating system of the grid) a universally unique identifier (UUID). This unique identifier may help other nodes and external entities (devices, users, etc.) to identify the node and distinguish it from other nodes. When a node is connected to the grid, the node may share its unique identifier with the other nodes in the grid. Since each node may share its unique identifier, each node may know the unique identifier of every other node on the grid. Unique identifiers may also designate a hierarchy of each of the nodes (e.g., backup control nodes) within the grid. For example, the unique identifiers of each of the backup control nodes may be stored in a list of backup control nodes to indicate an order in which the backup control nodes will take over for a failed primary control node to become a new primary control node. But, a hierarchy of nodes may also be determined using methods other than using the unique identifiers of the nodes. For example, the hierarchy may be predetermined, or may be assigned based on other predetermined factors.

The grid may add new machines at any time (e.g., initiated from any control node). Upon adding a new node to the grid, the control node may first add the new node to its table of grid nodes. The control node may also then notify every other control node about the new node. The nodes receiving the notification may acknowledge that they have updated their configuration information.

Primary control node 402 may, for example, transmit one or more communications to backup control nodes 404, 406 (and, for example, to other control or worker nodes 412-420 within the communications grid). Such communications may be sent periodically, at fixed time intervals, between known fixed stages of the project's execution, among other protocols. The communications transmitted by primary control node 402 may be of varied types and may include a variety of types of information. For example, primary control node 402 may transmit snapshots (e.g., status information) of the communications grid so that backup control node 404 always has a recent snapshot of the communications grid. The snapshot or grid status may include, for example, the structure of the grid (including, for example, the worker nodes 410-420 in the communications grid, unique identifiers of the worker nodes 410-420, or their relationships with the primary control node 402) and the status of a project (including, for example, the status of each worker node's portion of the project). The snapshot may also include analysis or results received from worker nodes 410-420 in the communications grid. The backup control nodes 404, 406 may receive and store the backup data received from the primary control node 402. The backup control nodes 404, 406 may transmit a request for such a snapshot (or other information) from the primary control node 402, or the primary control node 402 may send such information periodically to the backup control nodes 404, 406.

As noted, the backup data may allow a backup control node 404, 406 to take over as primary control node if the primary control node 402 fails without requiring the communications grid to start the project over from scratch. If the primary control node 402 fails, the backup control node 404, 406 that will take over as primary control node may retrieve the most recent version of the snapshot received from the primary control node 402 and use the snapshot to continue the project from the stage of the project indicated by the backup data. This may prevent failure of the project as a whole.

A backup control node 404, 406 may use various methods to determine that the primary control node 402 has failed. In one example of such a method, the primary control node 402 may transmit (e.g., periodically) a communication to the backup control node 404, 406 that indicates that the primary control node 402 is working and has not failed, such as a heartbeat communication. The backup control node 404, 406 may determine that the primary control node 402 has failed if the backup control node has not received a heartbeat communication for a certain predetermined period of time. Alternatively, a backup control node 404, 406 may also receive a communication from the primary control node 402 itself (before it failed) or from a worker node 410-420 that the primary control node 402 has failed, for example because the primary control node 402 has failed to communicate with the worker node 410-420.

Different methods may be performed to determine which backup control node of a set of backup control nodes (e.g., backup control nodes 404, 406) can take over for failed primary control node 402 and become the new primary control node. For example, the new primary control node may be chosen based on a ranking or “hierarchy” of backup control nodes based on their unique identifiers. In an alternative example, a backup control node may be assigned to be the new primary control node by another device in the communications grid or from an external device (e.g., a system infrastructure or an end user, such as a server or computer, controlling the communications grid). In another alternative example, the backup control node that takes over as the new primary control node may be designated based on bandwidth or other statistics about the communications grid.

A worker node within the communications grid may also fail. If a worker node fails, work being performed by the failed worker node may be redistributed amongst the operational worker nodes. In an alternative example, the primary control node may transmit a communication to each of the operable worker nodes still on the communications grid that each of the worker nodes should purposefully fail also. After each of the worker nodes fail, they may each retrieve their most recent saved checkpoint of their status and re-start the project from that checkpoint to minimize lost progress on the project being executed. In some examples, a communications grid computing system 400 can be used for visualizing machine-learning models and behavior.

FIG. 5 is a flow chart of an example of a process for adjusting a communications grid or a work project in a communications grid after a failure of a node according to some aspects. The process may include, for example, receiving grid status information including a project status of a portion of a project being executed by a node in the communications grid, as described in operation 502. For example, a control node (e.g., a backup control node connected to a primary control node and a worker node on a communications grid) may receive grid status information, where the grid status information includes a project status of the primary control node or a project status of the worker node. The project status of the primary control node and the project status of the worker node may include a status of one or more portions of a project being executed by the primary and worker nodes in the communications grid. The process may also include storing the grid status information, as described in operation 504. For example, a control node (e.g., a backup control node) may store the received grid status information locally within the control node. Alternatively, the grid status information may be sent to another device for storage where the control node may have access to the information.

The process may also include receiving a failure communication corresponding to a node in the communications grid in operation 506. For example, a node may receive a failure communication including an indication that the primary control node has failed, prompting a backup control node to take over for the primary control node. In an alternative embodiment, a node may receive a failure that a worker node has failed, prompting a control node to reassign the work being performed by the worker node. The process may also include reassigning a node or a portion of the project being executed by the failed node, as described in operation 508. For example, a control node may designate the backup control node as a new primary control node based on the failure communication upon receiving the failure communication. If the failed node is a worker node, a control node may identify a project status of the failed worker node using the snapshot of the communications grid, where the project status of the failed worker node includes a status of a portion of the project being executed by the failed worker node at the failure time.

The process may also include receiving updated grid status information based on the reassignment, as described in operation 510, and transmitting a set of instructions based on the updated grid status information to one or more nodes in the communications grid, as described in operation 512. The updated grid status information may include an updated project status of the primary control node or an updated project status of the worker node. The updated information may be transmitted to the other nodes in the grid to update their stale stored information.

FIG. 6 is a block diagram of a portion of a communications grid computing system 600 including a control node and a worker node according to some aspects. Communications grid 600 computing system includes one control node (control node 602) and one worker node (worker node 610) for purposes of illustration, but may include more worker and/or control nodes. The control node 602 is communicatively connected to worker node 610 via communication path 650. Therefore, control node 602 may transmit information (e.g., related to the communications grid or notifications), to and receive information from worker node 610 via communication path 650.

Similar to in FIG. 4, communications grid computing system (or just “communications grid”) 600 includes data processing nodes (control node 602 and worker node 610). Nodes 602 and 610 comprise multi-core data processors. Each node 602 and 610 includes a grid-enabled software component (GESC) 620 that executes on the data processor associated with that node and interfaces with buffer memory 622 also associated with that node. Each node 602 and 610 includes database management software (DBMS) 628 that executes on a database server (not shown) at control node 602 and on a database server (not shown) at worker node 610.

Each node also includes a data store 624. Data stores 624, similar to network-attached data stores 110 in FIG. 1 and data stores 235 in FIG. 2, are used to store data to be processed by the nodes in the computing environment. Data stores 624 may also store any intermediate or final data generated by the computing system after being processed, for example in non-volatile memory. However in certain examples, the configuration of the grid computing environment allows its operations to be performed such that intermediate and final data results can be stored solely in volatile memory (e.g., RAM), without a requirement that intermediate or final data results be stored to non-volatile types of memory. Storing such data in volatile memory may be useful in certain situations, such as when the grid receives queries (e.g., ad hoc) from a client and when responses, which are generated by processing large amounts of data, need to be generated quickly or on-the-fly. In such a situation, the grid may be configured to retain the data within memory so that responses can be generated at different levels of detail and so that a client may interactively query against this information.

Each node also includes a user-defined function (UDF) 626. The UDF provides a mechanism for the DMBS 628 to transfer data to or receive data from the database stored in the data stores 624 that are managed by the DBMS. For example, UDF 626 can be invoked by the DBMS to provide data to the GESC for processing. The UDF 626 may establish a socket connection (not shown) with the GESC to transfer the data. Alternatively, the UDF 626 can transfer data to the GESC by writing data to shared memory accessible by both the UDF and the GESC.

The GESC 620 at the nodes 602 and 610 may be connected via a network, such as network 108 shown in FIG. 1. Therefore, nodes 602 and 610 can communicate with each other via the network using a predetermined communication protocol such as, for example, the Message Passing Interface (MPI). Each GESC 620 can engage in point-to-point communication with the GESC at another node or in collective communication with multiple GESCs via the network. The GESC 620 at each node may contain identical (or nearly identical) software instructions. Each node may be capable of operating as either a control node or a worker node. The GESC at the control node 602 can communicate, over a communication path 652, with a client device 630. More specifically, control node 602 may communicate with client application 632 hosted by the client device 630 to receive queries and to respond to those queries after processing large amounts of data.

DMBS 628 may control the creation, maintenance, and use of database or data structure (not shown) within nodes 602 or 610. The database may organize data stored in data stores 624. The DMBS 628 at control node 602 may accept requests for data and transfer the appropriate data for the request. With such a process, collections of data may be distributed across multiple physical locations. In this example, each node 602 and 610 stores a portion of the total data managed by the management system in its associated data store 624.

Furthermore, the DBMS may be responsible for protecting against data loss using replication techniques. Replication includes providing a backup copy of data stored on one node on one or more other nodes. Therefore, if one node fails, the data from the failed node can be recovered from a replicated copy residing at another node. However, as described herein with respect to FIG. 4, data or status information for each node in the communications grid may also be shared with each node on the grid.

FIG. 7 is a flow chart of an example of a process for executing a data analysis or a processing project according to some aspects. As described with respect to FIG. 6, the GESC at the control node may transmit data with a client device (e.g., client device 630) to receive queries for executing a project and to respond to those queries after large amounts of data have been processed. The query may be transmitted to the control node, where the query may include a request for executing a project, as described in operation 702. The query can contain instructions on the type of data analysis to be performed in the project and whether the project should be executed using the grid-based computing environment, as shown in operation 704.

To initiate the project, the control node may determine if the query requests use of the grid-based computing environment to execute the project. If the determination is no, then the control node initiates execution of the project in a solo environment (e.g., at the control node), as described in operation 710. If the determination is yes, the control node may initiate execution of the project in the grid-based computing environment, as described in operation 706. In such a situation, the request may include a requested configuration of the grid. For example, the request may include a number of control nodes and a number of worker nodes to be used in the grid when executing the project. After the project has been completed, the control node may transmit results of the analysis yielded by the grid, as described in operation 708. Whether the project is executed in a solo or grid-based environment, the control node provides the results of the project.

As noted with respect to FIG. 2, the computing environments described herein may collect data (e.g., as received from network devices, such as sensors, such as network devices 204-209 in FIG. 2, and client devices or other sources) to be processed as part of a data analytics project, and data may be received in real time as part of a streaming analytics environment (e.g., ESP). Data may be collected using a variety of sources as communicated via different kinds of networks or locally, such as on a real-time streaming basis. For example, network devices may receive data periodically from network device sensors as the sensors continuously sense, monitor and track changes in their environments. More specifically, an increasing number of distributed applications develop or produce continuously flowing data from distributed sources by applying queries to the data before distributing the data to geographically distributed recipients. An event stream processing engine (ESPE) may continuously apply the queries to the data as it is received and determines which entities should receive the data. Client or other devices may also subscribe to the ESPE or other devices processing ESP data so that they can receive data after processing, based on for example the entities determined by the processing engine. For example, client devices 230 in FIG. 2 may subscribe to the ESPE in computing environment 214. In another example, event subscription devices 1024 a-c, described further with respect to FIG. 10, may also subscribe to the ESPE. The ESPE may determine or define how input data or event streams from network devices or other publishers (e.g., network devices 204-209 in FIG. 2) are transformed into meaningful output data to be consumed by subscribers, such as for example client devices 230 in FIG. 2.

FIG. 8 is a block diagram including components of an Event Stream Processing Engine (ESPE) according to some aspects. ESPE 800 may include one or more projects 802. A project may be described as a second-level container in an engine model managed by ESPE 800 where a thread pool size for the project may be defined by a user. Each project of the one or more projects 802 may include one or more continuous queries 804 that contain data flows, which are data transformations of incoming event streams. The one or more continuous queries 804 may include one or more source windows 806 and one or more derived windows 808.

The ESPE may receive streaming data over a period of time related to certain events, such as events or other data sensed by one or more network devices. The ESPE may perform operations associated with processing data created by the one or more devices. For example, the ESPE may receive data from the one or more network devices 204-209 shown in FIG. 2. As noted, the network devices may include sensors that sense different aspects of their environments, and may collect data over time based on those sensed observations. For example, the ESPE may be implemented within one or more of machines 220 and 240 shown in FIG. 2. The ESPE may be implemented within such a machine by an ESP application. An ESP application may embed an ESPE with its own dedicated thread pool or pools into its application space where the main application thread can do application-specific work and the ESPE processes event streams at least by creating an instance of a model into processing objects.

The engine container is the top-level container in a model that manages the resources of the one or more projects 802. In an illustrative example, there may be only one ESPE 800 for each instance of the ESP application, and ESPE 800 may have a unique engine name. Additionally, the one or more projects 802 may each have unique project names, and each query may have a unique continuous query name and begin with a uniquely named source window of the one or more source windows 806. ESPE 800 may or may not be persistent.

Continuous query modeling involves defining directed graphs of windows for event stream manipulation and transformation. A window in the context of event stream manipulation and transformation is a processing node in an event stream processing model. A window in a continuous query can perform aggregations, computations, pattern-matching, and other operations on data flowing through the window. A continuous query may be described as a directed graph of source, relational, pattern matching, and procedural windows. The one or more source windows 806 and the one or more derived windows 808 represent continuously executing queries that generate updates to a query result set as new event blocks stream through ESPE 800. A directed graph, for example, is a set of nodes connected by edges, where the edges have a direction associated with them.

An event object may be described as a packet of data accessible as a collection of fields, with at least one of the fields defined as a key or unique identifier (ID). The event object may be created using a variety of formats including binary, alphanumeric, XML, etc. Each event object may include one or more fields designated as a primary identifier (ID) for the event so ESPE 800 can support operation codes (opcodes) for events including insert, update, upsert, and delete. Upsert opcodes update the event if the key field already exists; otherwise, the event is inserted. For illustration, an event object may be a packed binary representation of a set of field values and include both metadata and field data associated with an event. The metadata may include an opcode indicating if the event represents an insert, update, delete, or upsert, a set of flags indicating if the event is a normal, partial-update, or a retention generated event from retention policy management, and a set of microsecond timestamps that can be used for latency measurements.

An event block object may be described as a grouping or package of event objects. An event stream may be described as a flow of event block objects. A continuous query of the one or more continuous queries 804 transforms a source event stream made up of streaming event block objects published into ESPE 800 into one or more output event streams using the one or more source windows 806 and the one or more derived windows 808. A continuous query can also be thought of as data flow modeling.

The one or more source windows 806 are at the top of the directed graph and have no windows feeding into them. Event streams are published into the one or more source windows 806, and from there, the event streams may be directed to the next set of connected windows as defined by the directed graph. The one or more derived windows 808 are all instantiated windows that are not source windows and that have other windows streaming events into them. The one or more derived windows 808 may perform computations or transformations on the incoming event streams. The one or more derived windows 808 transform event streams based on the window type (that is operators such as join, filter, compute, aggregate, copy, pattern match, procedural, union, etc.) and window settings. As event streams are published into ESPE 800, they are continuously queried, and the resulting sets of derived windows in these queries are continuously updated.

FIG. 9 is a flow chart of an example of a process including operations performed by an event stream processing engine according to some aspects. As noted, the ESPE 800 (or an associated ESP application) defines how input event streams are transformed into meaningful output event streams. More specifically, the ESP application may define how input event streams from publishers (e.g., network devices providing sensed data) are transformed into meaningful output event streams consumed by subscribers (e.g., a data analytics project being executed by a machine or set of machines).

Within the application, a user may interact with one or more user interface windows presented to the user in a display under control of the ESPE independently or through a browser application in an order selectable by the user. For example, a user may execute an ESP application, which causes presentation of a first user interface window, which may include a plurality of menus and selectors such as drop down menus, buttons, text boxes, hyperlinks, etc. associated with the ESP application as understood by a person of skill in the art. Various operations may be performed in parallel, for example, using a plurality of threads.

At operation 900, an ESP application may define and start an ESPE, thereby instantiating an ESPE at a device, such as machine 220 and/or 240. In an operation 902, the engine container is created. For illustration, ESPE 800 may be instantiated using a function call that specifies the engine container as a manager for the model.

In an operation 904, the one or more continuous queries 804 are instantiated by ESPE 800 as a model. The one or more continuous queries 804 may be instantiated with a dedicated thread pool or pools that generate updates as new events stream through ESPE 800. For illustration, the one or more continuous queries 804 may be created to model business processing logic within ESPE 800, to predict events within ESPE 800, to model a physical system within ESPE 800, to predict the physical system state within ESPE 800, etc. For example, as noted, ESPE 800 may be used to support sensor data monitoring and management (e.g., sensing may include force, torque, load, steach, position, temperature, air pressure, fluid flow, chemical properties, resistance, electromagnetic fields, radiation, irradiance, proximity, acoustics, moisture, distance, speed, vibrations, acceleration, electrical potential, or electrical current, etc.).

ESPE 800 may analyze and process events in motion or “event streams.” Instead of storing data and running queries against the stored data, ESPE 800 may store queries and stream data through them to allow continuous analysis of data as it is received. The one or more source windows 806 and the one or more derived windows 808 may be created based on the relational, pattern matching, and procedural algorithms that transform the input event streams into the output event streams to model, simulate, score, test, predict, etc. based on the continuous query model defined and application to the streamed data.

In an operation 906, a publish/subscribe (pub/sub) capability is initialized for ESPE 800. In an illustrative embodiment, a pub/sub capability is initialized for each project of the one or more projects 802. To initialize and enable pub/sub capability for ESPE 800, a port number may be provided. Pub/sub clients can use a host name of an ESP device running the ESPE and the port number to establish pub/sub connections to ESPE 800.

FIG. 10 is a block diagram of an ESP system 1000 interfacing between publishing device 1022 and event subscription devices 1024 a-c according to some aspects. ESP system 1000 may include ESP subsystem 1001, publishing device 1022, an event subscription device A 1024 a, an event subscription device B 1024 b, and an event subscription device C 1024 c. Input event streams are output to ESP subsystem 1001 by publishing device 1022. In alternative embodiments, the input event streams may be created by a plurality of publishing devices. The plurality of publishing devices further may publish event streams to other ESP devices. The one or more continuous queries instantiated by ESPE 800 may analyze and process the input event streams to form output event streams output to event subscription device A 1024 a, event subscription device B 1024 b, and event subscription device C 1024 c. ESP system 1000 may include a greater or a fewer number of event subscription devices of event subscription devices.

Publish-subscribe is a message-oriented interaction paradigm based on indirect addressing. Processed data recipients specify their interest in receiving information from ESPE 800 by subscribing to specific classes of events, while information sources publish events to ESPE 800 without directly addressing the receiving parties. ESPE 800 coordinates the interactions and processes the data. In some cases, the data source receives confirmation that the published information has been received by a data recipient.

A publish/subscribe API may be described as a library that enables an event publisher, such as publishing device 1022, to publish event streams into ESPE 800 or an event subscriber, such as event subscription device A 1024 a, event subscription device B 1024 b, and event subscription device C 1024 c, to subscribe to event streams from ESPE 800. For illustration, one or more publish/subscribe APIs may be defined. Using the publish/subscribe API, an event publishing application may publish event streams into a running event stream processor project source window of ESPE 800, and the event subscription application may subscribe to an event stream processor project source window of ESPE 800.

The publish/subscribe API provides cross-platform connectivity and endianness compatibility between ESP application and other networked applications, such as event publishing applications instantiated at publishing device 1022, and event subscription applications instantiated at one or more of event subscription device A 1024 a, event subscription device B 1024 b, and event subscription device C 1024 c.

Referring back to FIG. 9, operation 906 initializes the publish/subscribe capability of ESPE 800. In an operation 908, the one or more projects 802 are started. The one or more started projects may run in the background on an ESP device. In an operation 910, an event block object is received from one or more computing device of the publishing device 1022.

ESP subsystem 1001 may include a publishing client 1002, ESPE 800, a subscribing client A 1004, a subscribing client B 1006, and a subscribing client C 1008. Publishing client 1002 may be started by an event publishing application executing at publishing device 1022 using the publish/subscribe API. Subscribing client A 1004 may be started by an event subscription application A, executing at event subscription device A 1024 a using the publish/subscribe API. Subscribing client B 1006 may be started by an event subscription application B executing at event subscription device B 1024 b using the publish/subscribe API. Subscribing client C 1008 may be started by an event subscription application C executing at event subscription device C 1024 c using the publish/subscribe API.

An event block object containing one or more event objects is injected into a source window of the one or more source windows 806 from an instance of an event publishing application on publishing device 1022. The event block object may be generated, for example, by the event publishing application and may be received by publishing client 1002. A unique ID may be maintained as the event block object is passed between the one or more source windows 806 and/or the one or more derived windows 808 of ESPE 800, and to subscribing client A 1004, subscribing client B 1006, and subscribing client C 1008 and to event subscription device A 1024 a, event subscription device B 1024 b, and event subscription device C 1024 c. Publishing client 1002 may further generate and include a unique embedded transaction ID in the event block object as the event block object is processed by a continuous query, as well as the unique ID that publishing device 1022 assigned to the event block object.

In an operation 912, the event block object is processed through the one or more continuous queries 804. In an operation 914, the processed event block object is output to one or more computing devices of the event subscription devices 1024 a-c. For example, subscribing client A 1004, subscribing client B 1006, and subscribing client C 1008 may send the received event block object to event subscription device A 1024 a, event subscription device B 1024 b, and event subscription device C 1024 c, respectively.

ESPE 800 maintains the event block containership aspect of the received event blocks from when the event block is published into a source window and works its way through the directed graph defined by the one or more continuous queries 804 with the various event translations before being output to subscribers. Subscribers can correlate a group of subscribed events back to a group of published events by comparing the unique ID of the event block object that a publisher, such as publishing device 1022, attached to the event block object with the event block ID received by the subscriber.

In an operation 916, a determination is made concerning whether or not processing is stopped. If processing is not stopped, processing continues in operation 910 to continue receiving the one or more event streams containing event block objects from the, for example, one or more network devices. If processing is stopped, processing continues in an operation 918. In operation 918, the started projects are stopped. In operation 920, the ESPE is shutdown.

As noted, in some examples, big data is processed for an analytics project after the data is received and stored. In other examples, distributed applications process continuously flowing data in real-time from distributed sources by applying queries to the data before distributing the data to geographically distributed recipients. As noted, an event stream processing engine (ESPE) may continuously apply the queries to the data as it is received and determines which entities receive the processed data. This allows for large amounts of data being received and/or collected in a variety of environments to be processed and distributed in real time. For example, as shown with respect to FIG. 2, data may be collected from network devices that may include devices within the internet of things, such as devices within a home automation network. However, such data may be collected from a variety of different resources in a variety of different environments. In any such situation, embodiments of the present technology allow for real-time processing of such data.

Aspects of the present disclosure provide technical solutions to technical problems, such as computing problems that arise when an ESP device fails which results in a complete service interruption and potentially significant data loss. The data loss can be catastrophic when the streamed data is supporting mission critical operations, such as those in support of an ongoing manufacturing or drilling operation. An example of an ESP system achieves a rapid and seamless failover of ESPE running at the plurality of ESP devices without service interruption or data loss, thus significantly improving the reliability of an operational system that relies on the live or real-time processing of the data streams. The event publishing systems, the event subscribing systems, and each ESPE not executing at a failed ESP device are not aware of or effected by the failed ESP device. The ESP system may include thousands of event publishing systems and event subscribing systems. The ESP system keeps the failover logic and awareness within the boundaries of out-messaging network connector and out-messaging network device.

In one example embodiment, a system is provided to support a failover when event stream processing (ESP) event blocks. The system includes, but is not limited to, an out-messaging network device and a computing device. The computing device includes, but is not limited to, one or more processors and one or more computer-readable mediums operably coupled to the one or more processor. The processor is configured to execute an ESP engine (ESPE). The computer-readable medium has instructions stored thereon that, when executed by the processor, cause the computing device to support the failover. An event block object is received from the ESPE that includes a unique identifier. A first status of the computing device as active or standby is determined. When the first status is active, a second status of the computing device as newly active or not newly active is determined. Newly active is determined when the computing device is switched from a standby status to an active status. When the second status is newly active, a last published event block object identifier that uniquely identifies a last published event block object is determined. A next event block object is selected from a non-transitory computer-readable medium accessible by the computing device. The next event block object has an event block object identifier that is greater than the determined last published event block object identifier. The selected next event block object is published to an out-messaging network device. When the second status of the computing device is not newly active, the received event block object is published to the out-messaging network device. When the first status of the computing device is standby, the received event block object is stored in the non-transitory computer-readable medium.

FIG. 11 is a flow chart of another example of a process for computer-based visualization of machine-learning models and behavior according to some aspects. Other examples can involve more steps, fewer steps, different steps, or a different order of the steps shown in FIG. 11.

In block 1102, a processing device teaches (e.g., trains) a machine-learning model using a teaching dataset (e.g., training dataset). Examples of the machine-learning model can include a deep neural-network; a recurrent neural-network; a feed-forward neural-network; a convolutional neural-network; a classifier, such as a Support Vector Machine or nearest neighbor classifier; a decision tree; an ensemble or other combination of machine-learning models; or any combination of these. The teaching dataset can include input-output pairs, where each input-output pair includes (i) input data correlated to (ii) a desired output. The input data can be formed from elements, with each element including one or more words, image portions, sound portions, or any combination of these. For example, the input data can be a sentence formed from elements that are words or phrases. As another example, the input data can be an image formed from elements that are image portions. In some examples, the desired output can be a particular category into which the input data falls, or another type of output. The input-output pairs can be iteratively supplied to the machine-learning model to tune various weights that form the machine-learning model, thereby teaching the model.

In block 1104, the processing device determines a test input to provide to the machine-learning model. The test input can include an input-output pair for testing the machine-learning model. The test input may come from the teaching dataset, a validation dataset for validating the accuracy of the machine-learning model, or another source.

In block 1106, the processing device determines weights for some or all of the elements in the teaching dataset. An element's weight can indicate how strongly the element tends to influence a class determination for a particular class of content. A higher weight can indicate that the element tends to more strongly influence the class determination, while a lower weight can indicate that the element tends to less strongly influence the class determination. As one example, the teaching dataset can include movie reviews, which can be one class of content. And some words (e.g., actor, set, camera, or director) will have greater influence on a sentiment determination for movie reviews than other words (e.g., surfer, desk, or telephone). For instance, movies starring certain actors may have more-positive reviews than moves with less-popular actors, indicating that actor names have a relatively high influence on move-review sentiments. In this example, weights can be assigned to each element in the teaching dataset indicating that element's influence on the movie-review class of content.

In some examples, the processing device can determine the weight for an element using the following equation:

$w_{i} = {\max\limits_{C_{k}}\left( {\log\left( \frac{P\left( {t_{i},C_{k}} \right)}{{P\left( t_{i} \right)}{P\left( C_{k} \right)}} \right)} \right)}$ where w_(i) is a weight for an element t_(i); C_(k) is the teaching data that belongs to a particular class of content (k); P(t_(i), C_(k)) is a percentage of the teaching data that includes the element t_(i) and belongs to the class of content k; and P(t_(i)) is an amount of the teaching data that has element t_(i), divided by the size of the teaching dataset. This equation can assign higher weights to elements that are representative of a particular class of content, and lower weights to elements that appear frequently in the teaching data regardless of the class of content.

The processing device can determine a respective weight for each element in the teaching data, independently of the test input, using the abovementioned equation. This approach can be referred to as a static approach, because the weights may not depend on or otherwise change with the test input. The resulting list of elements and corresponding weights can be referred to as a static list. The static approach may be particularly useful for fast run-time applications, since the static list can be determined prior to run-time and then subsequently referenced quickly during run-time.

One example of a static list is shown in FIG. 12. In the table shown in FIG. 12, column 1202 indicates the elements, column 1204 indicates the frequency in which the elements appeared in the teaching data, column 1206 indicates the number of pieces of teaching data having the elements, and column 1208 indicates the weights for the elements. A piece of teaching data can be an observation in the teaching data. Higher weights can indicate greater influence on a particular class of content, while lower weights can indicate lesser influence on a particular class of content. For example, in FIG. 12, the word “pattern” has a higher weight than the word “pics,” indicating that “pattern” is more useful in predicting the class value than the word “pics” for this particular teaching-data. In other words, while “pics” can be present in both positive and negative reviews (e.g., possible class-values), the word “pattern” occurs more in reviews that belong to one of those class values. But other weighting schemes are possible.

In other examples, the processing device can determine the weights based on the test input (e.g., as discussed in greater detail below). This can be referred to as a dynamic approach, because the weights may depend on or otherwise change with the test input. The resulting list of elements and corresponding weights can be referred to as a dynamic list. The dynamic approach may be particularly useful if the test input is from a different class of content than the teaching data. For example, if the teaching data relates to movie reviews, but the test input is a product review, the static list may not yield desirable results because movie-review terminology (e.g., sound, actors, or lighting) would be weighted higher than product-review terminology (e.g., shipping time, packaging quality, or battery). It may therefore be desirable to have weights that change with the test input's class of content.

One example of a process for generating a dynamic list can involve first identifying a portion of the teaching dataset that is similar to the test input. This can be achieved by, for example, using keyword matches, query likelihood modeling, sequential dependency modeling, or any combination of these. Query likelihood modeling can involve creating a language model for each piece of data in a collection. The language model can then be used to determine the likelihood that a piece of data in the collection is relevant given a query. In some examples, the processing device can use query likelihood modeling to determine similarity scores for some or all of the input data in the input-output pairs in the teaching dataset. Each similarity score can indicate a similarity of the input data to the test input. For example, the test input can be [“I waited way too long. There is no place to sit. I am not going back.”]. The processing device can use this test input as a query and search for input data in the teaching dataset that is similar to the test input. Using this approach, the processing device can determine that, for example, the input data [“The queue is always long here. Be prepared to wait outside.”] has a higher similarity score, while the input data [“Awesome place with so many seats. Worth every penny.”] has a lower similarity score. After determining the similarity scores for some or all of the input data, the processing device can identify a subset of the input data that has similarity scores exceeding a predefined threshold. The predefined threshold can be associated with a high amount of similarity to the test input. For example, the processing device can identify 125 pieces of input data that have similarity scores exceeding a predefined threshold.

After identifying the subset of input data with similarity scores exceeding the predefined threshold, the processing device can rank the subset of the input data in order of similarity to the test input. For example, the processing device can rank the 125 pieces of input data in the subset from most to least similar to the test input. The processing device can then determine the top X pieces of input data in the subset that are most similar to the test input, where X can be a predefined value. For example, the processing device can determine the top 65 pieces of input data that are most similar to the test input. Next, the processing device can determine a weight for each element in the top X pieces of input data, for example, using the abovementioned equation. Finally, the processing device can then incorporate these elements and weights into a list, which is the dynamic list. Other examples can involve more steps, fewer steps, different steps, or a different order of the steps discussed above.

In block 1108, the processing device generates an analysis dataset based on the weights for the elements (e.g., in the static or dynamic list generated above). For example, the processing device can sort the elements by weight. One example of elements sorted by weight is shown in FIG. 13. The processing device can then select, for the analysis dataset, the elements that have weights above a predetermined threshold. In one example, the predetermined threshold can be the top 10% or 20% of elements. The predetermined threshold can be empirically determined based on the teaching dataset or determined using another approach. One example of program code for implementing a process to generate the analysis dataset is shown in FIG. 14.

In block 1110, the processing device provides the test input to the machine-learning model to determine a baseline confidence-score for the machine-learning model. A baseline confidence-score can be the probability of the predicted class of the original test input (e.g., before making any changes to the elements of the test input). As a particular example, the processing device can provide the test input to the machine-learning model to obtain an output, which may be a classification of the test input into a certain category. The machine-learning model may also provide a confidence score (e.g., a probability, such as 94%) indicating how certain the machine-learning model is that it produced the correct classification. This confidence score can be used as a baseline confidence-score for the machine-learning model.

In block 1112, the processing device determines an overlapping element in both the analysis dataset and the test input. For example, if the word “refund” is in the analysis dataset and the test input, the processing device would identify this word as an overlapping element.

In block 1114, the processing device generates a modified version of the test input that excludes the overlapping element (e.g., one instance of the overlapping element or all instances of the overlapping element). For example, if the test input includes the sentence “I want a refund,” the processing device can remove the word “refund” to generate a modified version of the test input that is “I want a.” As another example, if the test input is an image of a cat and the overlapping element is a black spot, the processing device can generate a modified version of the test input that excludes the black spot (e.g., by filling in the black spot with white or another color).

In block 1116, the processing device provides the modified version of the test input into the machine-learning model to determine an effect of the overlapping element on the output (e.g., the confidence score, classification, or both) of the machine-learning model. For example, the processing device may have previously determined a baseline confidence-score of 94% for the test input “I want a refund” belonging to a negative-sentiment class. The processing device can then provide a modified version of the test input that is “I want a” to the machine-learning model to obtain an output. The output can include a classification for the test input, along with a confidence score indicating how certain the machine-learning model is about the classification. The confidence score may be higher or lower than the baseline confidence-score. For example, the confidence score may be 12%, indicating that the machine-learning model is only 12% sure that the phrase “i want a” belongs to a negative-sentiment class. The large drop from 94% certainty to 12% certainty can indicate that the word “refund” is highly important in that sentence to produce the correct classification. So, removal of the word “refund” heavily impacts the certainty of the machine-learning model.

In some examples, the processing device can determine that the overlapping element has a large effect on the output of the machine-learning model if (i) the classification shifts from one classification to another classification, (ii) the confidence score is significantly more or less than the baseline confidence-score, or (iii) both of these. In contrast, the processing device can determine that the overlapping element has a minimal effect on the output of the machine-learning model if (i) the classification remains the same, (ii) the confidence score is not significantly more or less than (e.g., within 5% from) the baseline confidence-score, or (iii) both of these.

In some examples, if the classification shifts from an incorrect classification to a correct classification, the confidence score is less than the baseline confidence-score, or both of these, it may indicate that the overlapping element has a negative effect on the output of the machine-learning model. For example, it may indicate that the overlapping element is confusing to the machine-learning model. In contrast, if the classification shifts from a correct classification to an incorrect classification, the confidence score is more than the baseline confidence-score, or both of these, it may indicate that the overlapping element has a positive effect on the output of the machine-learning model.

In block 1118, the processing device adds the overlapping element to a first dataset or a second dataset based on the effect of the overlapping element on the output of the machine-learning model. For example, the processing device can include the overlapping element in the first dataset if the overlapping element has a first effect (e.g., a large or positive effect) on the output of the machine-learning model. The processing device can include the overlapping element in the second dataset if the overlapping element has a second effect (e.g., a minimal or negative effect) on the output of the machine-learning model. One example of program code for implementing this process is shown in FIG. 15. In this exemplary program code, the array usefullndexes is a list of elements that influence the machine-learning model toward a correct classification. The array confusingIndexes is a list of elements that are confusing to the machine-learning model and influence the machine-learning model toward an incorrect classification. The array usefullndexes can be the first database and the array confusingIndexes can be the second database, in some examples.

In block 1120, the processing device can determine if there is another overlapping element in both the analysis dataset and the test input. If so, the process can repeat operations 1112-1120. One example of this iterative process is shown in FIG. 16. In FIG. 16, an analysis dataset 1602 has words w₄, w₂₃, and w_(k), which are also in various locations in a test input 1604, as indicated using dashed lines. As a result, the processing device can generate three modified-versions of the test input in which the words w₄, w₂₃, and w_(k) are removed, respectively. The processing device can feed each of the three modified-versions of the test input into a machine-learning model 1606, which may have been previously trained, to determine a corresponding classification, all of which are designated as classifications 1608 in FIG. 16. In some examples, if classification A is the correct classification for the first test-input, it may indicate that w₄ has minimal influence on how the machine-learning model 1606 classifies data. And if classifications B-C are wrong for the second and third test-inputs, it may indicate that w₂₃ and w_(k) both have a significant influence on how the machine-learning model 1606 classifies data. A designer of the machine-learning model 1606 can use this information to better understand why the machine-learning model is producing certain results.

In some examples, the processing device implements the above process to test some or all of the elements in the analysis dataset in order to determine the element's influence on the machine-learning model's output. And because the analysis dataset may only be a relatively small portion of the initial teaching-dataset, the number of elements that are tested using this process can be significantly less than testing every element in the teaching dataset. This can considerably reduce the processing time and memory required to assess how input elements influence the machine-learning model's output, for example, as compared to a brute-force approach in which every element in the teaching dataset is tested. One example of a comparison of these two approaches is shown in FIG. 17. In this example, column 1702 indicates processor-execution times when using an analysis dataset of the present disclosure, and column 1704 indicates processor-execution times when using the brute-force approach. As shown, using the analysis dataset results in processor-execution times that are 33.97 times faster, on average, than using the brute-force approach. This is a dramatic reduction in processing time.

Returning to FIG. 11, if the process device determines that there are no more overlapping elements in both the analysis dataset and the test input, the process can proceed to block 1122, in which the processing device can generate a graphical user interface (GUI). In some examples, the GUI can include a textual representation of the test input. The GUI can also include a set of elements from the first dataset, whereby the set of elements are visually emphasized in a particular manner (e.g., using a particular color, font, styling, or any combination of these) to indicate an effect that the set of elements has on the output of (e.g., the confidence score of) the machine-learning model. Additionally or alternatively, the GUI can include another set of elements from the second dataset, whereby the other set of elements are visually emphasized in another manner to indicate another effect that the set of elements has on the output of the machine-learning model.

Some examples of GUIs 1800 a-d are shown in FIG. 18. Each GUI 1800 a-d includes a paragraph of text, which is the test input. The highlighting in these GUIs 1800 a-d can depend on machine-learning model's predictions. If the machine-learning model predicts a correct classification (e.g., a classification value that is the same as the ground truth), the words that steered the machine-learning model towards the correct classification can be highlighted without dashed borders. These words can be referred to as helpful words. And the words that steered the model towards an incorrect classification can be highlighted in dashed borders. These words can be referred to as confusing words. If the machine-learning model predicts an incorrect classification (e.g., a classification value that does not match the ground truth), the helpful words can be highlighted without dashed borders, and the confusing words can be highlighted within dashed borders. In other GUIs, the helpful words can be depicted or highlighted in a particular color, and the confusing words can be depicted or highlighted in another color. Any number and combination of visual elements can be used to identify (or distinguish) the helpful words from confusing words.

In some examples, the GUIs 1800 a-d can help to identify correlations between certain elements (e.g., words) and sentiments that may not be intuitive to a designer. For example, in GUI 1800 a, the test input is a review that has a negative-sentiment overall, but the machine-learning model in this example incorrectly predicted that the test input has a positive sentiment. The confusing words are highlighted in dashed borders, and influenced the machine-learning model's output towards an incorrect (e.g., positive sentiment) classification. The helpful words are highlighted without dashed borders, and influenced the machine-learning model's output towards a correct (e.g., negative sentiment) classification. It may be surprising to a developer that the words “served,” acceptable,” and “even” have negative connotations according to the GUI 1800 a, and therefore tend to influence the test input toward a negative-sentiment classification. Removing these words may decrease the confidence score of the test input toward the correct classification. It may also be surprising that the words “too,” “thing,” “check,” “prices,” and “minutes” have positive connotations according to the GUI 1800 a, and therefore tend to influence the test input toward a positive-sentiment classification. Interestingly, the word “prices” confuses the machine-learning model so much that removing that word can turn the incorrect classification of this model (e.g. positive) to a correct classification (e.g. negative) for this review. A designer may be able to make a number of modifications to the machine-learning model based on these insights.

In GUI 1800 b, the test input is also a review that has a negative-sentiment overall, so the correct classification is a negative-sentiment classification. But, again, the machine-learning model incorrectly predicted a positive-sentiment classification. A developer may analyze the GUI 1800 b to determine why the machine-learning model predicted the wrong classification. It may be surprising to a developer that the word “burned” has a positive connotation according to the GUI 1800 b, and is thus influencing the test input toward a positive-sentiment classification. The word “burned” is therefore confusing to the machine-learning model, because it is influencing the test input toward the wrong sentiment-classification.

In GUI 1800 c, the test input is another review with a negative-sentiment overall, so the correct classification is a negative-sentiment classification. In this example, the machine-learning model correctly predicted a negative-sentiment classification. Since none of the highlighted words in this example change the predicted classification (e.g., from negative to positive), a developer may focus on how the highlighted words impact the machine-learning model's confidence in the predicted class. It may be surprising to the developer that the words “why,” “average,” “consider” and “provide” have negative connotations according to the GUI 1800 c, and are thus helpful words that improve the confidence of the predicted class. It may also be surprising to the developer that the words “nothing,” “oil,” “Italian,” and “should” have positive connotations, and are thus confusing words that decrease the machine-learning model's confidence in the predicted class.

In GUI 1800 d, the test input is yet another review with a negative-sentiment overall, so the correct classification is a negative-sentiment classification. The words “slow” and “average” have negative connotations according to the GUI 1800 c, and thus influence the test input toward a negative-sentiment classification. The words “tried” and “cheese” have positive connotations according to the GUI 1800 c, and are thus confusing to the machine-learning model.

Another example of a GUI 1900 is shown in FIG. 19. A designer may be able to use the GUI 1900 to make sense of the baseline confidence-score. For example, the machine-learning model may initially classify this test input as having a positive sentiment, with a baseline confidence-score of 0.88. When the word “crap” is removed, the confidence score may become 0.96. And when the word “hip” is removed, the confidence score may become 0.95. And when the word “bad” is removed, the confidence score may become 0.96. Even in the presence of these words, the machine-learning model determined the correct classification. But the visualization can help the designer understand which words that lowered the baseline confidence-score to 0.88.

A designer can refer to the GUI 1900 to determine which words positively influence the machine-learning model's output, negatively influence the machine-learning model's output, or both. The designer can then adjust the machine-learning model, as needed. Additionally or alternatively, the processing device can automatically adjust the machine-learning model based on the first dataset, the second dataset, or both. For example, the processing device can adjust a characteristic of the machine-learning model based on at least one of the first dataset or the second dataset to improve the accuracy, confidence score, or both of the machine-learning model. Adjusting the characteristic may involve adjusting (i) a weight (e.g., a node weight or connection weight) in the machine-learning model, (ii) a topology (e.g., a configuration of nodes, layers, or both) of the machine-learning model, or (iii) both of these. For example, the processing device can increase a weight for a node in the machine-learning model based on the node being associated with an element listed in the first dataset, decrease a weight for a connection in the machine-learning model based on the connection being associated with an element listed in the second dataset, or both of these.

The foregoing description of certain examples, including illustrated examples, has been presented only for the purpose of illustration and description and is not intended to be exhaustive or to limit the disclosure to the precise forms disclosed. Numerous modifications, adaptations, and uses thereof will be apparent to those skilled in the art without departing from the scope of the disclosure. And the examples disclosed herein can be combined or rearranged to yield additional examples.

General

In the previous description, for the purposes of explanation, specific details are set forth in order to provide a thorough understanding of examples of the technology. But various examples can be practiced without these specific details. The figures and description are not intended to be restrictive.

The previous description provides examples that are not intended to limit the scope, applicability, or configuration of the disclosure. Rather, the previous description of the examples provides those skilled in the art with an enabling description for implementing an example. Various changes may be made in the function and arrangement of elements without departing from the spirit and scope of the technology as set forth in the appended claims.

Specific details are given in the previous description to provide a thorough understanding of the examples. But the examples may be practiced without these specific details. For example, circuits, systems, networks, processes, and other components can be shown as components in block diagram form to prevent obscuring the examples in unnecessary detail. In other examples, well-known circuits, processes, algorithms, structures, and techniques may be shown without unnecessary detail in order to avoid obscuring the examples.

Also, individual examples may have been described as a process that is depicted as a flowchart, a flow diagram, a data flow diagram, a structure diagram, or a block diagram. Although a flowchart can describe the operations as a sequential process, many of the operations can be performed in parallel or concurrently. In addition, the order of the operations can be re-arranged. And a process can have more or fewer operations than are depicted in a figure. A process can correspond to a method, a function, a procedure, a subroutine, a subprogram, etc. When a process corresponds to a function, its termination can correspond to a return of the function to the calling function or the main function.

Systems depicted in some of the figures can be provided in various configurations. In some examples, the systems can be configured as a distributed system where one or more components of the system are distributed across one or more networks in a cloud computing system. 

The invention claimed is:
 1. A method for visualizing machine-learning model behavior, the method comprising: generating, by a processing device, an analysis dataset by: receiving a teaching dataset for use in teaching a machine-leaning model, the teaching dataset including pieces of input data correlated to output categories, wherein each piece of input data is correlated to one of the output categories, and wherein each piece of input data is formed from multiple elements; determining a plurality of weights for a plurality of elements in the teaching dataset, wherein each weight in the plurality of weights corresponds to a respective element in the plurality of elements and is determined by dividing (i) a first value that is determined based on a number of pieces of input data in the teaching dataset that both include the respective element and belong to a specific one of the output categories, by (ii) a second value that is determined based on a total number of pieces of input data in the teaching dataset that have the respective element; determining a subset of elements among the plurality of elements having corresponding weights above a predefined threshold; and based on determining that the subset of elements have the corresponding weights above the predefined threshold, incorporating the subset of elements into the analysis dataset, the analysis dataset being separate from the teaching dataset and only including the subset of elements; determining, by the processing device, a baseline confidence-score associated with a test input by: providing the test input to a trained machine-learning model to obtain the baseline confidence-score as output from the trained machine-learning model, wherein the test input is obtained from a source other than the analysis dataset and the teaching dataset, and wherein the trained machine-learning model was trained using the teaching dataset prior to receiving the test input; subsequent to determining the baseline confidence-score, determining, by the processing device, how the subset of elements in the analysis dataset effect the baseline confidence-score associated with the test input by: comparing the test input to the analysis dataset to identify one or more overlapping elements in both the test input and the analysis dataset; and for each overlapping element among the one or more overlapping elements in both the test input and the analysis dataset: generating a modified version of the test input that excludes the overlapping element; providing the modified version of the test input to the trained machine-learning model to determine an effect of the overlapping element on the baseline confidence-score; and adding the overlapping element to (i) a first dataset in response to the effect of the overlapping element on the baseline confidence-score being positive, or (ii) a second dataset in response to the effect of the overlapping element on the baseline confidence-score being negative; and generating, by the processing device, a graphical user interface that visually depicts: the test input; the first dataset as having a positive effect on the baseline confidence-score; and the second dataset as having a negative effect on the baseline confidence-score.
 2. The method of claim 1, wherein: the pieces of input data are blocks of text; and each element in the plurality of elements is a textual term formed from one or more words.
 3. The method of claim 2, wherein the graphical user interface comprises a textual representation of the test input having: a first set of words visually emphasized in a first manner to indicate the positive effect of the first set of words on the baseline confidence-score, the first set of words being in the first dataset; and a second set of words visually emphasized in a second manner that is different from the first manner to indicate the negative effect of the second set of words on the baseline confidence-score, the second set of words being in the second dataset.
 4. The method of claim 1, wherein the trained machine-learning model is a recurrent neural-network.
 5. The method of claim 1, wherein determining the plurality of weights comprises determining a respective weight for every element in the teaching dataset.
 6. The method of claim 1, wherein the pieces of input data are images, and the elements are portions of the images.
 7. The method of claim 1, further comprising, prior to determining the plurality of weights for the plurality of elements, selecting the plurality of elements from among the teaching dataset at least in part by: determining a plurality of similarity scores between the test input and the pieces of input data in the teaching dataset, each similarity score in the plurality of similarity scores indicating a similarity between the test input and a respective piece of input data in the teaching dataset; selecting a subset of the pieces of input data that have similarity scores above a predefined threshold; and identifying the plurality of elements from within the subset of the pieces of input data.
 8. The method of claim 1, further comprising adjusting a characteristic of the trained machine-learning model based on at least one of the first dataset or the second dataset to improve a confidence score of the trained machine-learning model.
 9. The method of claim 8, wherein adjusting the characteristic of the trained machine-learning model comprises at least one of (i) increasing a weight in the trained machine-learning model, wherein the weight is associated with an element in the first dataset; or (ii) decreasing another weight in the trained machine-learning model, wherein the other weight is associated with another element in the second dataset.
 10. The method of claim 1, wherein the test input includes at least one element that is excluded from the analysis dataset, and wherein the analysis dataset is separate from the trained machine-learning model.
 11. A system comprising: a processing device; and a memory device on which instructions executable by the processing device are stored for causing the processing device to: generate an analysis dataset by: receiving a teaching dataset for use in teaching a machine-leaning model, the teaching dataset including pieces of input data correlated to output categories, wherein each piece of input data is correlated to one of the output categories, and wherein each piece of input data is formed from multiple elements; determining a plurality of weights for a plurality of elements in the teaching dataset, wherein each weight in the plurality of weights corresponds to a respective element in the plurality of elements and is determined by dividing (i) a first value that is determined based on a number of pieces of input data in the teaching dataset that both include the respective element and belong to a specific one of the output categories, by (ii) a second value that is determined based on a total number of pieces of input data in the teaching dataset that have the respective element; determining a subset of elements among the plurality of elements having corresponding weights above a predefined threshold; and based on determining that the subset of elements have the corresponding weights above the predefined threshold, incorporating the subset of elements into the analysis dataset, the analysis dataset being separate from the teaching dataset and only including the subset of elements; determine a baseline confidence-score associated with a test input by: providing the test input to a trained machine-learning model to obtain the baseline confidence-score as output from the trained machine-learning model, wherein the test input is obtained from a source other than the analysis dataset and the teaching dataset, and wherein the trained machine-learning model was trained using the teaching dataset prior to receiving the test input; subsequent to determining the baseline confidence-score, determine how the subset of elements in the analysis dataset effect the baseline confidence-score associated with the test input by: comparing the test input to the analysis dataset to identify one or more overlapping elements in both the test input and the analysis dataset; and for each overlapping element among the one or more overlapping elements in both the test input and the analysis dataset: generating a modified version of the test input that excludes the overlapping element; providing the modified version of the test input to the trained machine-learning model to determine an effect of the overlapping element on the baseline confidence-score; and adding the overlapping element to (i) a first dataset in response to the effect of the overlapping element on the baseline confidence-score being positive, or (ii) a second dataset in response to the effect of the overlapping element on the baseline confidence-score being negative; and generate a graphical user interface that visually depicts: the test input; the first dataset as having a positive effect on the baseline confidence-score; and the second dataset as having a negative effect on the baseline confidence-score.
 12. The system of claim 11, wherein: the pieces of input data are blocks of text; and each element in the plurality of elements is a textual term formed from one or more words.
 13. The system of claim 12, wherein the graphical user interface comprises a textual representation of the test input in which words in the first dataset are highlighted using a first color to indicate the positive effect of the words on the baseline confidence-score and words in the second dataset are highlighted using a second color to indicate the negative effect of the words on the baseline confidence-score, the second color being different from the first color.
 14. The system of claim 11, wherein the trained machine-learning model is a recurrent neural-network.
 15. The system of claim 11, wherein determining the plurality of weights comprises determining a respective weight for every element in the teaching dataset.
 16. The system of claim 11, wherein the pieces of input data are images, and the elements are portions of the images.
 17. The system of claim 11, wherein each weight in the plurality of weights is determined by calculating a logarithm of a result of dividing the first value by the second value.
 18. The system of claim 11, wherein the memory device further comprises instructions that are executable by the processing device for causing the processing device to, prior to determining the plurality of weights for the plurality of elements, selecting the plurality of elements from among the teaching dataset at least in part by: determine a plurality of similarity scores between the test input and the pieces of input data in the teaching dataset, each similarity score in the plurality of similarity scores indicating a similarity between the test input and a respective piece of input data in the teaching dataset; select a subset of the pieces of input data that have similarity scores above a predefined threshold; and identify the plurality of elements from within the subset of the pieces of input data.
 19. The system of claim 11, wherein the memory device further comprises instructions that are executable by the processing device for causing the processing device to adjust a characteristic of the trained machine-learning model based on at least one of the first dataset or the second dataset to improve a confidence score of the trained machine-learning model.
 20. The system of claim 19, wherein the memory device further comprises instructions that are executable by the processing device for causing the processing device to adjust the characteristic of the trained machine-learning model by at least one of (i) increasing a weight in the trained machine-learning model, wherein the weight is associated with an element in the first dataset; or (ii) decreasing another weight in the trained machine-learning model, wherein the other weight is associated with another element in the second dataset.
 21. A non-transitory computer-readable medium comprising program code that is executable by a processing device for causing the processing device to: generate an analysis dataset by: receiving a teaching dataset for use in teaching a machine-leaning model, the teaching dataset including pieces of input data correlated to output categories, wherein each piece of input data is correlated to one of the output categories, and wherein each piece of input data is formed from multiple elements; determining a plurality of weights for a plurality of elements in the teaching dataset, wherein each weight in the plurality of weights corresponds to a respective element in the plurality of elements and is determined by dividing (i) a first value that is determined based on a number of pieces of input data in the teaching dataset that both include the respective element and belong to a specific one of the output categories, by (ii) a second value that is determined based on a total number of pieces of input data in the teaching dataset that have the respective element; determining a subset of elements among the plurality of elements having corresponding weights above a predefined threshold; and based on determining that the subset of elements have the corresponding weights above the predefined threshold, incorporating the subset of elements into the analysis dataset, the analysis dataset being separate from the teaching dataset and only including the subset of elements; determine a baseline confidence-score associated with a test input by: providing the test input to a trained machine-learning model to obtain the baseline confidence-score as output from the trained machine-learning model, wherein the test input is obtained from a source other than the analysis dataset and the teaching dataset, and wherein the trained machine-learning model was trained using the teaching dataset prior to receiving the test input; subsequent to determining the baseline confidence-score, determine how the subset of elements in the analysis dataset effect the baseline confidence-score associated with the test input by: comparing the test input to the analysis dataset to identify one or more overlapping elements in both the test input and the analysis dataset; and for each overlapping element among the one or more overlapping elements in both the test input and the analysis dataset: generating a modified version of the test input that excludes the overlapping element; providing the modified version of the test input to the trained machine-learning model to determine an effect of the overlapping element on the baseline confidence-score; and adding the overlapping element to (i) a first dataset in response to the effect of the overlapping element on the baseline confidence-score being positive, or (ii) a second dataset in response to the effect of the overlapping element on the baseline confidence-score being negative; and generate a graphical user interface that visually depicts: the test input; the first dataset as having a positive effect on the baseline confidence-score; and the second dataset as having a negative effect on the baseline confidence-score.
 22. The non-transitory computer-readable medium of claim 21, wherein: the pieces of input data are blocks of text; and each element in the plurality of elements is a textual term formed from one or more words.
 23. The non-transitory computer-readable medium of claim 22, wherein the graphical user interface comprises a textual representation of the test input having: a first set of words visually emphasized in a first manner to indicate the positive effect of the first set of words on the baseline confidence-score, the first set of words being in the first dataset; and a second set of words visually emphasized in a second manner that is different from the first manner to indicate the negative effect of the second set of words on the baseline confidence-score, the second set of words being in the second dataset.
 24. The non-transitory computer-readable medium of claim 21, wherein the trained machine-learning model is a recurrent neural-network.
 25. The non-transitory computer-readable medium of claim 21, wherein determining the plurality of weights comprises determining a respective weight for every element in the teaching dataset.
 26. The non-transitory computer-readable medium of claim 21, wherein the pieces of input data are images, and the elements are portions of the images.
 27. The non-transitory computer-readable medium of claim 21, wherein each weight in the plurality of weights is determined by calculating a logarithm of a result of dividing the first value by the second value.
 28. The non-transitory computer-readable medium of claim 21, further comprising program code that is executable by the processing device for causing the processing device to, prior to determining the plurality of weights for the plurality of elements, selecting the plurality of elements from among the teaching dataset at least in part by: determine a plurality of similarity scores between the test input and the pieces of input data in the teaching dataset, each similarity score in the plurality of similarity scores indicating a similarity between the test input and a respective piece of input data in the teaching dataset; select a subset of the pieces of input data that have similarity scores above a predefined threshold; and identify the plurality of elements from within the subset of the pieces of input data.
 29. The non-transitory computer-readable medium of claim 21, further comprising program code that is executable by the processing device for causing the processing device to adjust a characteristic of the trained machine-learning model based on at least one of the first dataset or the second dataset to improve a confidence score of the trained machine-learning model.
 30. The non-transitory computer-readable medium of claim 29, further comprising program code that is executable by the processing device for causing the processing device to adjust the characteristic of the trained machine-learning model by at least one of (i) increasing a weight in the trained machine-learning model, wherein the weight is associated with an element in the first dataset; or (ii) decreasing another weight in the trained machine-learning model, wherein the other weight is associated with another element in the second dataset.
 31. A non-transitory computer-readable medium comprising program code that is executable by a processing device for causing the processing device to: teach a machine-learning model using a teaching dataset that has pieces of input data correlated to output categories, wherein each piece of input data is correlated to a particular output category and is formed from multiple elements; determine a plurality of weights for a plurality of elements in the teaching dataset, wherein each weight in the plurality of weights corresponds to a respective element in the plurality of elements and is determined by calculating a result of dividing (i) a first value determined based on a number of pieces of input data in the teaching dataset that both include the respective element and belong to a specific output category, by (ii) a second value determined based on a proportion of a total number of pieces of input data in the teaching data set that have the respective element; generate an analysis dataset that includes a subset of the plurality of elements that have corresponding weights that are above a predefined threshold; provide a test input to the machine-learning model to determine a baseline confidence-score of the machine-learning model, wherein the test input is obtained from a source other than the analysis dataset and the teaching dataset, the test input having one or more elements in the analysis dataset; for each overlapping element in both the analysis dataset and the test input: generate a modified version of the test input that excludes the overlapping element; provide the modified version of the test input to the machine-learning model to determine an effect of the overlapping element on the baseline confidence-score; and add the overlapping element to (i) a first dataset in response to the effect of the overlapping element on the baseline confidence-score being positive, or (ii) a second dataset in response to the effect of the overlapping element on the baseline confidence-score being negative; and generate a graphical user interface that visually depicts: the test input; the first dataset as having a positive effect on the baseline confidence-score; and the second dataset as having a negative effect on the baseline confidence-score. 